298 lines
13 KiB
Python
298 lines
13 KiB
Python
# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Tests for audio embedder."""
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import enum
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import os
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from typing import List, Tuple
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from unittest import mock
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from absl.testing import absltest
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from absl.testing import parameterized
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import numpy as np
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from scipy.io import wavfile
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from mediapipe.tasks.python.audio import audio_embedder
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from mediapipe.tasks.python.audio.core import audio_record
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from mediapipe.tasks.python.audio.core import audio_task_running_mode
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from mediapipe.tasks.python.components.containers import audio_data as audio_data_module
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from mediapipe.tasks.python.core import base_options as base_options_module
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from mediapipe.tasks.python.test import test_utils
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_AudioEmbedder = audio_embedder.AudioEmbedder
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_AudioEmbedderOptions = audio_embedder.AudioEmbedderOptions
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_AudioEmbedderResult = audio_embedder.AudioEmbedderResult
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_AudioData = audio_data_module.AudioData
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_AudioRecord = audio_record.AudioRecord
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_BaseOptions = base_options_module.BaseOptions
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_RUNNING_MODE = audio_task_running_mode.AudioTaskRunningMode
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_YAMNET_MODEL_FILE = 'yamnet_embedding_metadata.tflite'
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_YAMNET_MODEL_SAMPLE_RATE = 16000
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_SPEECH_WAV_16K_MONO = 'speech_16000_hz_mono.wav'
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_SPEECH_WAV_48K_MONO = 'speech_48000_hz_mono.wav'
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_TWO_HEADS_WAV_16K_MONO = 'two_heads_16000_hz_mono.wav'
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_TEST_DATA_DIR = 'mediapipe/tasks/testdata/audio'
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_YAMNET_NUM_OF_SAMPLES = 15600
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_MILLISECONDS_PER_SECOND = 1000
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# Tolerance for embedding vector coordinate values.
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_EPSILON = 3e-6
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class ModelFileType(enum.Enum):
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FILE_CONTENT = 1
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FILE_NAME = 2
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class AudioEmbedderTest(parameterized.TestCase):
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def setUp(self):
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super().setUp()
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self.yamnet_model_path = test_utils.get_test_data_path(
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os.path.join(_TEST_DATA_DIR, _YAMNET_MODEL_FILE))
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def _read_wav_file(self, file_name) -> _AudioData:
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sample_rate, buffer = wavfile.read(
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test_utils.get_test_data_path(os.path.join(_TEST_DATA_DIR, file_name)))
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return _AudioData.create_from_array(
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buffer.astype(float) / np.iinfo(np.int16).max, sample_rate)
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def _read_wav_file_as_stream(self, file_name) -> List[Tuple[_AudioData, int]]:
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sample_rate, buffer = wavfile.read(
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test_utils.get_test_data_path(os.path.join(_TEST_DATA_DIR, file_name)))
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audio_data_list = []
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start = 0
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step_size = _YAMNET_NUM_OF_SAMPLES * sample_rate / _YAMNET_MODEL_SAMPLE_RATE
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while start < len(buffer):
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end = min(start + (int)(step_size), len(buffer))
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audio_data_list.append((_AudioData.create_from_array(
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buffer[start:end].astype(float) / np.iinfo(np.int16).max,
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sample_rate), (int)(start / sample_rate * _MILLISECONDS_PER_SECOND)))
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start = end
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return audio_data_list
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def _check_embedding_value(self, result, expected_first_value):
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# Check embedding first value.
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self.assertAlmostEqual(
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result.embeddings[0].embedding[0], expected_first_value, delta=_EPSILON)
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def _check_embedding_size(self, result, quantize, expected_embedding_size):
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# Check embedding size.
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self.assertLen(result.embeddings, 1)
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embedding_result = result.embeddings[0]
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self.assertLen(embedding_result.embedding, expected_embedding_size)
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if quantize:
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self.assertEqual(embedding_result.embedding.dtype, np.uint8)
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else:
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self.assertEqual(embedding_result.embedding.dtype, float)
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def test_create_from_file_succeeds_with_valid_model_path(self):
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# Creates with default option and valid model file successfully.
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with _AudioEmbedder.create_from_model_path(
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self.yamnet_model_path) as embedder:
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self.assertIsInstance(embedder, _AudioEmbedder)
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def test_create_from_options_succeeds_with_valid_model_path(self):
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# Creates with options containing model file successfully.
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with _AudioEmbedder.create_from_options(
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_AudioEmbedderOptions(
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base_options=_BaseOptions(
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model_asset_path=self.yamnet_model_path))) as embedder:
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self.assertIsInstance(embedder, _AudioEmbedder)
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def test_create_from_options_fails_with_invalid_model_path(self):
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with self.assertRaisesRegex(
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RuntimeError, 'Unable to open file at /path/to/invalid/model.tflite'):
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base_options = _BaseOptions(
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model_asset_path='/path/to/invalid/model.tflite')
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options = _AudioEmbedderOptions(base_options=base_options)
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_AudioEmbedder.create_from_options(options)
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def test_create_from_options_succeeds_with_valid_model_content(self):
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# Creates with options containing model content successfully.
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with open(self.yamnet_model_path, 'rb') as f:
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base_options = _BaseOptions(model_asset_buffer=f.read())
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options = _AudioEmbedderOptions(base_options=base_options)
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embedder = _AudioEmbedder.create_from_options(options)
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self.assertIsInstance(embedder, _AudioEmbedder)
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@parameterized.parameters(
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# Same audio inputs but different sample rates.
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(False, False, ModelFileType.FILE_NAME, _SPEECH_WAV_16K_MONO,
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_SPEECH_WAV_48K_MONO, 1024, (0, 0)),
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(False, False, ModelFileType.FILE_CONTENT, _SPEECH_WAV_16K_MONO,
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_SPEECH_WAV_48K_MONO, 1024, (0, 0)))
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def test_embed_with_yamnet_model(self, l2_normalize, quantize,
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model_file_type, audio_file0, audio_file1,
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expected_size, expected_first_values):
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# Creates embedder.
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if model_file_type is ModelFileType.FILE_NAME:
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base_options = _BaseOptions(model_asset_path=self.yamnet_model_path)
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elif model_file_type is ModelFileType.FILE_CONTENT:
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with open(self.yamnet_model_path, 'rb') as f:
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model_content = f.read()
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base_options = _BaseOptions(model_asset_buffer=model_content)
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else:
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# Should never happen
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raise ValueError('model_file_type is invalid.')
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options = _AudioEmbedderOptions(
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base_options=base_options, l2_normalize=l2_normalize, quantize=quantize)
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with _AudioEmbedder.create_from_options(options) as embedder:
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embedding_result0_list = embedder.embed(self._read_wav_file(audio_file0))
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embedding_result1_list = embedder.embed(self._read_wav_file(audio_file1))
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# Checks embeddings.
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expected_result0_value, expected_result1_value = expected_first_values
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self._check_embedding_size(embedding_result0_list[0], quantize,
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expected_size)
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self._check_embedding_size(embedding_result1_list[0], quantize,
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expected_size)
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self._check_embedding_value(embedding_result0_list[0],
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expected_result0_value)
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self._check_embedding_value(embedding_result1_list[0],
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expected_result1_value)
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self.assertLen(embedding_result0_list, 5)
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self.assertLen(embedding_result1_list, 5)
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@mock.patch('sounddevice.InputStream', return_value=mock.MagicMock())
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def test_create_audio_record_from_embedder_succeeds(self, _):
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# Creates AudioRecord instance using the embedder successfully.
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with _AudioEmbedder.create_from_model_path(
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self.yamnet_model_path
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) as embedder:
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self.assertIsInstance(embedder, _AudioEmbedder)
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record = embedder.create_audio_record(1, 16000, 16000)
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self.assertIsInstance(record, _AudioRecord)
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self.assertEqual(record.channels, 1)
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self.assertEqual(record.sampling_rate, 16000)
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self.assertEqual(record.buffer_size, 16000)
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def test_embed_with_yamnet_model_and_different_inputs(self):
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with _AudioEmbedder.create_from_model_path(
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self.yamnet_model_path) as embedder:
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embedding_result0_list = embedder.embed(
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self._read_wav_file(_SPEECH_WAV_16K_MONO))
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embedding_result1_list = embedder.embed(
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self._read_wav_file(_TWO_HEADS_WAV_16K_MONO))
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self.assertLen(embedding_result0_list, 5)
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self.assertLen(embedding_result1_list, 1)
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def test_missing_sample_rate_in_audio_clips_mode(self):
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options = _AudioEmbedderOptions(
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base_options=_BaseOptions(model_asset_path=self.yamnet_model_path),
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running_mode=_RUNNING_MODE.AUDIO_CLIPS)
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with self.assertRaisesRegex(ValueError,
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r'Must provide the audio sample rate'):
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with _AudioEmbedder.create_from_options(options) as embedder:
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embedder.embed(_AudioData(buffer_length=100))
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def test_missing_sample_rate_in_audio_stream_mode(self):
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options = _AudioEmbedderOptions(
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base_options=_BaseOptions(model_asset_path=self.yamnet_model_path),
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running_mode=_RUNNING_MODE.AUDIO_STREAM,
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result_callback=mock.MagicMock())
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with self.assertRaisesRegex(ValueError,
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r'provide the audio sample rate in audio data'):
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with _AudioEmbedder.create_from_options(options) as embedder:
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embedder.embed(_AudioData(buffer_length=100))
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def test_missing_result_callback(self):
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options = _AudioEmbedderOptions(
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base_options=_BaseOptions(model_asset_path=self.yamnet_model_path),
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running_mode=_RUNNING_MODE.AUDIO_STREAM)
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with self.assertRaisesRegex(ValueError,
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r'result callback must be provided'):
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with _AudioEmbedder.create_from_options(options) as unused_embedder:
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pass
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def test_illegal_result_callback(self):
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options = _AudioEmbedderOptions(
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base_options=_BaseOptions(model_asset_path=self.yamnet_model_path),
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running_mode=_RUNNING_MODE.AUDIO_CLIPS,
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result_callback=mock.MagicMock())
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with self.assertRaisesRegex(ValueError,
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r'result callback should not be provided'):
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with _AudioEmbedder.create_from_options(options) as unused_embedder:
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pass
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def test_calling_embed_in_audio_stream_mode(self):
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options = _AudioEmbedderOptions(
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base_options=_BaseOptions(model_asset_path=self.yamnet_model_path),
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running_mode=_RUNNING_MODE.AUDIO_STREAM,
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result_callback=mock.MagicMock())
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with _AudioEmbedder.create_from_options(options) as embedder:
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with self.assertRaisesRegex(ValueError,
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r'not initialized with the audio clips mode'):
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embedder.embed(self._read_wav_file(_SPEECH_WAV_16K_MONO))
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def test_calling_embed_async_in_audio_clips_mode(self):
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options = _AudioEmbedderOptions(
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base_options=_BaseOptions(model_asset_path=self.yamnet_model_path),
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running_mode=_RUNNING_MODE.AUDIO_CLIPS)
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with _AudioEmbedder.create_from_options(options) as embedder:
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with self.assertRaisesRegex(
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ValueError, r'not initialized with the audio stream mode'):
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embedder.embed_async(self._read_wav_file(_SPEECH_WAV_16K_MONO), 0)
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def test_embed_async_calls_with_illegal_timestamp(self):
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options = _AudioEmbedderOptions(
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base_options=_BaseOptions(model_asset_path=self.yamnet_model_path),
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running_mode=_RUNNING_MODE.AUDIO_STREAM,
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result_callback=mock.MagicMock())
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with _AudioEmbedder.create_from_options(options) as embedder:
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embedder.embed_async(self._read_wav_file(_SPEECH_WAV_16K_MONO), 100)
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with self.assertRaisesRegex(
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ValueError, r'Input timestamp must be monotonically increasing'):
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embedder.embed_async(self._read_wav_file(_SPEECH_WAV_16K_MONO), 0)
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@parameterized.parameters(
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# Same audio inputs but different sample rates.
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(False, False, _SPEECH_WAV_16K_MONO, _SPEECH_WAV_48K_MONO))
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def test_embed_async(self, l2_normalize, quantize, audio_file0, audio_file1):
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embedding_result_list = []
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embedding_result_list_copy = embedding_result_list.copy()
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def save_result(result: _AudioEmbedderResult, timestamp_ms: int):
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result.timestamp_ms = timestamp_ms
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embedding_result_list.append(result)
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options = _AudioEmbedderOptions(
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base_options=_BaseOptions(model_asset_path=self.yamnet_model_path),
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running_mode=_RUNNING_MODE.AUDIO_STREAM,
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l2_normalize=l2_normalize,
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quantize=quantize,
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result_callback=save_result)
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with _AudioEmbedder.create_from_options(options) as embedder:
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audio_data0_list = self._read_wav_file_as_stream(audio_file0)
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for audio_data, timestamp_ms in audio_data0_list:
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embedder.embed_async(audio_data, timestamp_ms)
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embedding_result0_list = embedding_result_list
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with _AudioEmbedder.create_from_options(options) as embedder:
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audio_data1_list = self._read_wav_file_as_stream(audio_file1)
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embedding_result_list = embedding_result_list_copy
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for audio_data, timestamp_ms in audio_data1_list:
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embedder.embed_async(audio_data, timestamp_ms)
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embedding_result1_list = embedding_result_list
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self.assertLen(embedding_result0_list, 5)
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self.assertLen(embedding_result1_list, 5)
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if __name__ == '__main__':
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absltest.main()
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